CycleGAN Face-off
Xiaohan Jin, Ye Qi, Shangxuan Wu

TL;DR
This paper enhances CycleGAN-based face style transfer by improving adversarial training to better capture facial expressions and head poses, resulting in more consistent and stable transformation videos.
Contribution
It introduces novel improvements to CycleGAN training specifically tailored for facial expression and pose transfer, enhancing detail preservation and video stability.
Findings
Improved facial expression detail in generated videos
Enhanced head pose consistency
More stable and realistic face transformations
Abstract
Face-off is an interesting case of style transfer where the facial expressions and attributes of one person could be fully transformed to another face. We are interested in the unsupervised training process which only requires two sequences of unaligned video frames from each person and learns what shared attributes to extract automatically. In this project, we explored various improvements for adversarial training (i.e. CycleGAN[Zhu et al., 2017]) to capture details in facial expressions and head poses and thus generate transformation videos of higher consistency and stability.
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Face recognition and analysis · Domain Adaptation and Few-Shot Learning
